Through this paper, Andris reminds us that there is a huge gap between social networks (SNs) and GISystem (GIS). NS analysis always happens without many geographic considerations.
As Andris indicates, SN analysts rarely care about geographic features except for distance. However, most geographers may agree that distance is not enough to represent the geography of SN dynamics. Therefore, Andris note that we should integrate SN data into GIS for better modeling human social behaviors. However, why do we need better models? Even this paper present numerous case studies, they may not efficiently reveal the significance of the integration between SNs and GIS. In my perspectives, better understanding how human socialize, share information, and form social groups indicates great potentials to improve public participation by knowing citizens’ needs and enhance social justice by identifying power relationships in a more accurate way. Therefore, this framework contributes to realizing the goal of “digital government”, “open government”, or related rhetoric.
For constructing this framework, identifying the differences between SNs and GIS data and proposing “social flow” and “anthrospace” concepts are two most important parts in this paper. Andris give us critical insights of SNs and GIS data and explain how social flow and anthrospace work in GIS. But during the implementation process, technically, it is still a shallow convergence by geolocating SN nodes and storing location features in data tables. If there is no more specific method, the analysis of SN data in GIS can vary from case to case, which is hard to be rigor from my perspectives.
It is no doubt that Andris provides a significant framework for integrating SN data in GIS. However, without enough consideration of practical challenges, for example, how to deal with big data generated by huge amounts of social agents, we cannot widely adopt this framework to real-world projects and fully discover its potentials.